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However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms.<\/jats:p>","DOI":"10.3233\/jifs-219221","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T10:27:19Z","timestamp":1640341639000},"page":"4307-4313","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Ensemble-based deep meta learning for medical image segmentation"],"prefix":"10.1177","volume":"42","author":[{"given":"Usman","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Science, Electronic Engineering and Mathematical Science, Western Norway University of Applied Sciences, Bergen, Norway"}]},{"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electronic Engineering and Mathematical Science, Western Norway University of Applied Sciences, Bergen, Norway"}]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Mathematics &amp; Computer Science, Brandon University, Brandon, Canada"},{"name":"Research Centre for Interneural Computing, China Medical University, Taiwan"}]}],"member":"179","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"issue":"2019","key":"e_1_3_1_2_2","first-page":"480","article-title":"A meta-learning approach for selecting imagesegmentation algorithm","volume":"128","author":"Aguiar G.J.","unstructured":"AguiarG.J., MantovaniR.G., MasteliniS.M., deCarvalhoA.C., CamposG.F. and JuniorS.B., A meta-learning approach for selecting imagesegmentation algorithm, Pattern Recognition Letters128(2019), 480\u2013487.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"AhmedL. 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